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HomeEditors PickAgentic AIZ.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB

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Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB

By

Michal Sutter

October 13, 2026

Add as a preferred source on Google

Z.ai just released GLM-Legal. GLM-Legal is a legal AI model from Z.ai, built on GLM-5.3-Flash and trained for agentic legal work. It targets contract review, evidence analysis and compliance research. The key result is a 19.2% all-pass rate on Harvey’s Legal Agent Benchmark (LAB). That is nearly 3x its base model’s 6.7%. For developers, the catch is access: the API is application-only for now.

TL;DR

• Size: Built on GLM-5.3-Flash, a 320B-total, 18B-active MoE with a 1M-token context and 128K max output. Z.ai lists no separate GLM-Legal parameter count.
• Runs on: Z.ai’s hosted API only, by application. No public weights. The base model self-hosts on multi-GPU servers via vLLM or SGLang, with FP8 weights.
• Performance: Top-10 on Harvey LAB at a fraction of frontier cost. Within 2.3 points of the LegalBench leader.
• Best: 19.17% Harvey LAB task pass rate, #7 of 79 on Vals AI, at $0.15 per test.
• Worst: 41.83% on Vals AI’s Legal Research Bench, below its own base model (45.19%).
• Bottom line:
  • Best: frontier-class legal agent scores at about 1/128 the cost of Claude Fable 5.
  • Worst: gated beta with no price, no weights and slow runs (1h12m per task).

What is GLM-Legal?

GLM-Legal is a domain-specialized version of GLM-5.3-Flash for law firms, in-house counsel and compliance teams. The base is Z.ai’s first natively multimodal GLM-5 model. It has 320B total parameters with 18B active. It mixes sparse and linear attention to cut long-context serving cost. It reads text, images, video and files, and outputs text.

How was GLM-Legal trained?

Training ran in 2 stages:

• Mid-training on expanded legal corpora, to strengthen reasoning from facts to rules to conclusions.
• Post-training with reinforcement learning across tens of thousands of legal task environments.

The Z.ai team also describes the agent harness co-evolving with the model during post-training. Edge cases in training exposed harness bottlenecks. Harness fixes then exposed the model to more realistic constraints.

How does GLM-Legal score on Harvey’s Legal Agent Benchmark?

Harvey LAB is an open-source benchmark of 1,250 tasks across 24 practice areas. Over 75,000 expert-written rubric criteria grade the work. A task passes only if every criterion passes. Harvey calls this all-pass grading.

Vals AI runs the held-out set with 2 LLM judges and internet disabled. Agents get file tools plus

docx

,

pptx

and

xlsx

skills.

On that leaderboard, GLM-Legal scores 19.17% and ranks #7 of 79. Its base, GLM-5.3-Flash, scores 6.67%. The 12.5-point lift is the core claim, and an independent evaluator reproduced it.

Z.ai’s launch chart shows GLM-Legal ahead of Kimi K3 (12.92%), Claude Fable 5 (11.25%) and GPT-6 Astra (5.42%). Those figures match Vals AI. The chart omits 6 models ranked higher, led by Muse Spark 1.2 at 25.42%.

GLM-Legal also passes 92.5% of individual criteria. Top models sit near 90% to 95% on that measure.

Does GLM-Legal match frontier models on LegalBench?

Roughly. Z.ai claims parity on LegalBench and the Chinese-focused PLawBench without publishing numbers. Vals AI scores GLM-Legal at 86.23% on LegalBench. Claude Fable 5 leads at 88.56%. Kimi K3 scores 86.21%.

How does GLM-Legal compare with Kimi K3, Claude Fable 5 and GPT-6 Astra?

All benchmark scores, costs and durations are from Vals AI’s runs as of October 12, 2026. Vals ran both GLM models at $0.075 / $0.25 per 1M tokens.

How can developers access GLM-Legal?

Law firms, legal-tech companies and in-house teams can apply for API access. Z.ai has published no price. No GLM-Legal repository exists on Hugging Face as of October 13, 2026. Z.ai says GLM-Legal will come to AutoClaw, its desktop agent app, but gave no date.

The base model is available today at $0.15 / $0.50 per 1M tokens. Teams can prototype pipelines on it, then swap the model ID later.

Key Takeaways

• GLM-Legal scores 19.17% on Harvey LAB, up from 6.67% for its base.
• It ranks #7 of 79 on Vals AI, behind 6 Meta and Google models.
• It costs $0.15 per Harvey LAB test, versus $19.23 for Claude Fable 5.
• Legal Research Bench dropped 3.36 points versus GLM-5.3-Flash.
• Access is application-only, with no published price or weights.

Check out the Z.ai tech blog, the Vals AI Harvey LAB leaderboard and the GLM-5.3-Flash model card. Apply for API access here.. All credit goes to the researcher of this project. Also,feel free to follow us on**Twitterand don’t forget to join our150k+ML SubRedditand Subscribe toour Newsletter. Wait! are you on telegram?now you can join us on telegram as well.**

Michal Sutter

+ posts Bio ⮌

Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.

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DEPLOY THIS MODEL

GLM-Legal

PARTIAL — Application-Only Beta Proprietary (no public license)

Self-host · No GLM-Legal weights; base FP8 MoE via vLLM/SGLang, multi-GPU

API · Z.ai hosted API by application only; no price published

Local · No local GLM-Legal; base GGUF/EXL3 quants exist via llama.cpp

Z Z.ai Consultation Apply for GLM-Legal API access (design-partner form)↗AC AutoClaw Z.ai desktop agent; GLM-Legal integration coming soon↗HF Hugging Face Base model only: zai-org/GLM-5.3-Flash weights (FP8)↗

Verified Oct 14, 2026 · Marktechpost

Datalab Releases OmniParseBench: An Open OCR Benchmark With 16,288 Pass/Fail Tests

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Text extracted automatically; images, tables and formatting may be missing. Original: https://www.marktechpost.com/2026/10/13/zai-glm-legal-harvey-lab-legal-ai-model/